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Prognosis

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Machine learning based association between inflammation indicators (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality in arthritis patients with hypertension: NHANES 1999-2018.

Frontiers in public health
BACKGROUND: This study aimed to evaluate the relationship between CBC-derived inflammatory markers (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality (ACM) risk in arthritis (AR) patients with hypertensive (HTN) using data from the NHANES.

Early prediction of neoadjuvant therapy response in breast cancer using MRI-based neural networks: data from the ACRIN 6698 trial and a prospective Chinese cohort.

Breast cancer research : BCR
BACKGROUND: Early prediction of treatment response to neoadjuvant therapy (NAT) in breast cancer patients can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a MRI-based enhanced self-attention network (MESN) for ...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Scientific reports
Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making. Existing studies have predominantly focused on EEG for assessing brain injury, with some exploring...

Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors.

Cardiovascular diabetology
BACKGROUND: Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality globally. Traditional risk models, primarily based on established risk factors, often lack the precision needed to accurately predict new-onset major advers...

An ensemble learning model to predict lymph node metastasis in early gastric cancer.

Scientific reports
Lymph node metastasis is a critical factor for determining therapeutic strategies and assessing the prognosis of early gastric cancer. This work aimed to establish a more dependable predictive model for identify lymph node metastasis in early gastric...

Identification of novel IL17-related genes as prognostic and therapeutic biomarkers of psoriasis using comprehensive bioinformatics analysis and machine learning.

Scientific reports
Psoriasis is a common chronic skin disorder with a polygenic background. It is widely acknowledged that Th17/IL-17A axis plays a key role in the pathogenesis of psoriasis. However, numerous regulatory genes upstream of the pathway remain undiscovered...

Data-Driven Prognostication in Distal Medium Vessel Occlusions Using Explainable Machine Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Distal medium vessel occlusions (DMVOs) are estimated to cause acute ischemic stroke in 25%-40% of cases. Prognostic models can inform patient counseling and research by enabling outcome predictions. However, models designed s...

Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review.

European respiratory review : an official journal of the European Respiratory Society
BACKGROUND: Pleural diseases represent a significant healthcare burden, affecting over 350 000 patients annually in the US alone and requiring accurate diagnostic approaches for optimal management. Traditional imaging techniques have limitations in d...

Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma.

Frontiers in immunology
BACKGROUND: Unlike lung adenocarcinoma, patients with advanced squamous carcinoma exhibit a low proportion of driver gene positivity, with fewer effective treatment strategies available. Chemoimmunotherapy has now become the standard first-line treat...

Enlightened prognosis: Hepatitis prediction with an explainable machine learning approach.

PloS one
Hepatitis is a widespread inflammatory condition of the liver, presenting a formidable global health challenge. Accurate and timely detection of hepatitis is crucial for effective patient management, yet existing methods exhibit limitations that unde...